TY - GEN
T1 - Long-tailed Regression with Ensembles for Monocular Height Estimation from Single Remote Sensing Images
AU - Chen, Sining
AU - Shi, Yilei
AU - Zhu, Xiao Xiang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We focus on the long-tailed distribution problem of monocular height estimation from single remote sensing images. The long-tailed distribution results in dramatically large errors for pixels of high buildings. To cope with that, we propose an ensemble network making use of building footprints as the auxiliary information, which leads to improvements of the results. Together with the designed soft label, the network is robust to noises in building footprints. The proposed network is simple, yet practical and able to mitigate the long-tailed effect in monocular height estimation.
AB - We focus on the long-tailed distribution problem of monocular height estimation from single remote sensing images. The long-tailed distribution results in dramatically large errors for pixels of high buildings. To cope with that, we propose an ensemble network making use of building footprints as the auxiliary information, which leads to improvements of the results. Together with the designed soft label, the network is robust to noises in building footprints. The proposed network is simple, yet practical and able to mitigate the long-tailed effect in monocular height estimation.
KW - ensemble
KW - long-tailed regression
KW - monocular height estimation
KW - remote sensing
UR - https://www.scopus.com/pages/publications/85163705519
U2 - 10.1109/JURSE57346.2023.10144186
DO - 10.1109/JURSE57346.2023.10144186
M3 - Conference contribution
AN - SCOPUS:85163705519
T3 - 2023 Joint Urban Remote Sensing Event, JURSE 2023
BT - 2023 Joint Urban Remote Sensing Event, JURSE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Joint Urban Remote Sensing Event, JURSE 2023
Y2 - 17 May 2023 through 19 May 2023
ER -